Unverified50% confidenceFactExact time
Temperature is a hyperparameter that controls the randomness of a language model's output; it acts on logits by dividing the logit by temperature T before softmax normalization—when T<1 the distribution sharpens and output becomes more deterministic, when T>1 the distribution flattens and output becomes more random.
1
Sources
50%
Confidence
Long-term
Relevance
10/7/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedWhen a large language model generates each token, it samples from a probability distribution; the temperature parameter controls the degree of randomness—higher temperature produces more diverse outputs, while lower temperature produces more deterministic ones.77% similarUnverified大语言模型（LLM）由于temperature、top-p、top-k等采样策略带有随机性，同一提示词在两次调用中可能产生截然不同的响应75% similarUnverifiedThe core of model distillation lies in the application of the temperature softmax function, which softens the probability distribution through the temperature parameter T to reveal similarity relationships between classes73% similarUnverifiedLLM基于Transformer架构的自注意力机制和Softmax概率分布，通过温度参数控制采样随机性，温度趋近0时输出最高概率token，温度升高时引入更多随机性69% similarVerifiedWhen generating each token, LLMs randomly sample from a probability distribution according to a temperature parameter rather than selecting the deterministic output with the highest probability, thereby producing emergent properties.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/985417API
curl https://kongchang.com/api/v1/knowledge/claims/985417MCP
get_claim(id=985417)